Magnetic resonance imaging (MRI) is ideal for clinical imaging because it is information-rich and clinically safe. However, many MRI techniques cannot enter the realm of clinical utility because MRI has an intrinsically low data acquisition speed that may limit the spatial or temporal specificity of a clinical examination, introduce motion artifacts, and reduce the patient throughput. Over the years, a number of high-speed data acquisition techniques have been developed to address this fundamental challenge to clinical MRI. These include partial Fourier imaging, parallel imaging and compressed sensing. Among them, parallel imaging is the only technique that has successfully transformed clinical MRI by offering the capability of accelerating a single MRI scan by a factor of >2. Currently, two standard parallel imaging frameworks for clinical applications are SENSE and GRAPPA. Currently, all well-used high-speed imaging techniques implemented on available clinical MRI systems, e.g. GE-ASSET/ARC, SIEMENS-iPAT, and PHILIPS-SENSE/BLAST, are based on one of these two frameworks.
Parallel imaging follows a deterministic path to reconstruct images from undersampled data. This deterministic approach may meet a number of practical problems associated with unpredictable factors in clinical scans. For example, SENSE uses coil sensitivity profiles to calculate a deterministic relationship between reduced field of view (FOV) images and the final image. On a clinical MRI scanner, coil sensitivity calibration relies on a single calibration scan at the beginning of a clinical protocol. Unpredictable image information, scan parameters and patient motion in subsequent scans may invalidate the calibration, and produce artifacts in the reconstructed images. For this reason, most clinical MRI protocols with SENSE use a conservative acceleration factor (<3 in 2D imaging). For the same reason, many clinicians prefer GRAPPA with auto-calibration signals (ACS) acquired simultaneously with each clinical scan although this data acquisition scheme considerably slows down the entire clinical protocol. SENSE artifacts on a clinical scanner are introduced by unpredictable image information, acquisition parameters and motion during the clinical scans after calibration.
SENSE and GRAPPA accelerate MRI data acquisition by sampling data below the Nyquist criterion. The reconstruction from the undersampled data to an aliasing-free image relies on the spatial encoding provided by multi-channel coil sensitivity. This reconstruction relationship requires calibration using fully sampled data. In SENSE, a calibration scan is performed before the real scan. In GRAPPA, auto-calibration signals (ACS) are acquired simultaneously with the real scan. In these schemes, the calibration of reconstruction relies on a single set of low-resolution data acquired from the calibration scan or the ACS. The calibration for SENSE is performed at the beginning of the protocol and used for all the following scans. In GRAPPA, ACS data are acquired in every scan and each scan is reconstructed individually. All multi-scan data offer the capability of calibration because they share the same coil sensitivity information. If all of these data are efficiently used for calibration, more information about coil sensitivity can be extracted and the reconstruction may be improved. This also implies the repetitive ACS data acquisition may not be necessary, allowing for higher imaging acceleration. Therefore, SENSE and GRAPPA have not taken the most advantage of data availability in multi-scan imaging. Furthermore, it should be noted that multi-scan imaging data are acquired from the same human subject in a clinical protocol thereby sharing the anatomical structure information that may be used to further improve reconstruction. For example, the magnitude of most images in MRI dominates the phase because anatomical structure has few boundaries that may affect B0 field inhomogeneity. The k-space data, if without coil sensitivity, are thus nearly conjugate symmetric. This correlation between the original and the conjugate symmetric data has been used in partial Fourier imaging and is also shared by multi-scan imaging data. However, standard parallel imaging frameworks for clinical imaging have not benefited from this apparent information sharing. On currently available MRI systems, as SENSE and GRAPPA frameworks rely totally on the spatial encoding capability of coil sensitivity, their acceleration is physically limited by the configuration of a coil array in data acquisition.
In MRI, coil array design poses a physical limit to parallel imaging acceleration because reconstruction from undersampled data relies on the data relationship introduced by multi-channel coil sensitivities.
Information sharing has been frequently used in high-speed MRI. For example, SENSE uses coil sensitivity information shared by a calibration scan and clinical scans. GRAPPA uses the information shared by the ACS and the real scan data. A number of dynamic imaging techniques, such as keyhole, constrained reconstruction, dynamic imaging by modeling, UNFOLD, reduced FOV imaging, k-t SENSE/BLAST and k-t GRAPPA, . . . , etc., use the static or a priori information shared by all the images acquired from a dynamic scan. If slice gaps are small (close to zero), a multi-slice imaging scan may use image similarity between neighboring slices. Most of these techniques follow deterministic physical mechanisms to calculate or model the shared information across images. Because the unpredictable imaging contrast, resolution or geometry may interfere with the deterministic calculation or modeling, these techniques require either that the image information be removed, e.g. SENSE uses only coil sensitivity profiles without any image contrast information, or that the information is extracted only from those images with minor contrast difference (not as significant as the difference between T1 and T2 contrast), e.g. ACS data from the same scan (8,31), all dynamic images from the same dynamic scan (10,11,26), or neighboring slices with zero gaps (30). These prior strategies have been demonstrated to be effective in high-speed MRI for single-scan (static or dynamic) data acquisition.
In contrast to these frameworks, correlation imaging discussed in the present disclosure follows a statistical route to estimating the shared information from multi-scan imaging data that have dramatically different imaging contrast (e.g. T1 and T2 contrast difference), resolution or geometry. The statistical characterization of the average behavior of a large amount of imaging data reduces the interference from varying information providing a robust approach to utilizing information sharing to speed up multi-scan and multi-channel data acquisition in a clinical MRI protocol. The current disclosure provides the framework of correlation imaging for uniform undersampling, and introduces a practical approach to the statistical characterization of information sharing for image reconstruction. The presented experimental results demonstrate that correlation imaging offers the capability of using shared information across images with different contrast and resolution. Also demonstrated is the ability of correlation imaging to overcome the speed limit posed by a radiofrequency (RF) coil array because of the use of information beyond coil sensitivity in reconstruction.
In the framework of correlation imaging, correlation functions are used to mathematically describe a generic data relationship, and the reconstruction relies on the estimation of correlation functions from prior knowledge about imaging data. In a high-resolution brain imaging experiment using an 8-channel head coil array with at most 4 elements in any physical direction, it is demonstrated that a conventional parallel imaging technique performs well only if an acceleration factor≦4 is used, while the correlation-based reconstruction provides excellent image quality even with an acceleration factor far beyond that limit.